DBMS > Google BigQuery vs. Greenplum vs. Hive vs. InfinityDB vs. OushuDB

System Properties Comparison Google BigQuery vs. Greenplum vs. Hive vs. InfinityDB vs. OushuDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A Java embedded Key-Value Store which extends the Java Map interface

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Website

cloud.google.com/bigquery

greenplum.org

hive.apache.org

boilerbay.com

www.oushu.com/product/oushuDB

Technical documentation

cloud.google.com/bigquery/docs

docs.greenplum.org

cwiki.apache.org/confluence/display/Hive/Home

boilerbay.com/infinitydb/manual

www.oushu.com/documentation

Developer

Google

Pivotal Software Inc.

Apache Software Foundation

initially developed by Facebook

Boiler Bay Inc.

Oushu

Initial release

2010

2005

2012

2002

Current release

7.0.0, September 2023

3.1.3, April 2022

4.0

4.0.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

hosted

Linux

All OS with a Java VM

Linux

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

since Version 4.2

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

Full-featured ANSI SQL support

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

JDBC
ODBC
Thrift

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
Java
Perl
Python
R

C++
Java
PHP
Python

Java

C
C++

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

Kerberos, SSL and role based access

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